Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems

Cimen, Mustafa and Kirkbride, Christopher (2017) Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems. International Journal of Production Research, 55 (7). pp. 2034-2050. ISSN 0020-7543

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Abstract

An important issue in the manufacturing and supply chain literature concerns the optimisation of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions and settings. However, as systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimising single inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such a complex application area. Decisions involving the interrelated product inventories and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be readily obtained. We propose the methodology of approximate dynamic programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup simulation approach, ADP develops policies by utilising only a fraction of the computations required by classical dynamic programming. However, there are few studies in the literature that optimise production decisions in a stochastic, multi-factory, multi-product inventory system of this complexity. This paper aims to explore the feasibility and relevancy of ADP algorithms for this application. We present the results from numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies and to policies derived from a deterministic approximation of the problem.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Production Research
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 13/12/2016, available online: http://www.tandfonline.com/10.1080/00207543.2016.1264643
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2209
Subjects:
ID Code:
83498
Deposited By:
Deposited On:
08 Dec 2016 15:14
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Nov 2020 04:19